# An LSTM Network for Real-Time Odometry Estimation

## TL;DR

This paper introduces a deep learning framework using RCNNs with LSTM for real-time vehicle odometry estimation from 2D laser scanner data, achieving competitive accuracy without GPU acceleration.

## Contribution

It presents a novel RCNN-based approach that combines CNN feature extraction with LSTM modeling for odometry from 2D laser scans, enabling real-time performance.

## Key findings

- Runs in real-time without GPU acceleration
- Achieves competitive odometry accuracy
- Effective feature extraction from 2D laser data

## Abstract

The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, light-weight and low-cost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long Short-Term Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in real-time without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.